{"id":"W4387962351","doi":"10.1200/op.2023.19.11_suppl.587","title":"Multi-task machine learning of the electronic medical record to predict future symptoms among patients with cancer.","year":2023,"lang":"en","type":"article","venue":"JCO Oncology Practice","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; Princess Margaret Cancer Centre","funders":"","keywords":"Medicine; Interquartile range; Medical record; Medical diagnosis; Cohort; Psychological intervention; Electronic medical record; Physical therapy; Internal medicine; Emergency medicine; Radiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002774271,0.000682182,0.00074978,0.001389501,0.0002739877,0.0007676299,0.0006787446,0.000861157,0.001334531],"category_scores_gemma":[0.008577891,0.0002680222,0.0009564374,0.0008548512,0.0001947806,0.0007664695,0.0007372765,0.001214904,0.000680247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005992874,"about_ca_system_score_gemma":0.0007330148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006367283,"about_ca_topic_score_gemma":0.007558532,"domain_scores_codex":[0.9990824,0.0004081096,0.00009438881,0.0002141655,0.00009872561,0.0001021401],"domain_scores_gemma":[0.9963101,0.002506628,0.0004046524,0.0002414357,0.0003372775,0.0001999691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002695699,0.002665421,0.4072543,0.0004329582,0.001073256,0.0006851664,0.0003135975,0.2151522,0.002165492,0.0005118722,0.01604478,0.3510052],"study_design_scores_gemma":[0.00004458909,0.0003675603,0.0361971,0.00002963175,0.0000579745,0.0001102673,0.00006299743,0.9611407,0.0005378694,0.0007726506,0.0006628404,0.00001586421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9311373,0.003190879,0.05011648,0.003623233,0.0003379743,0.0003659029,0.007570668,0.001243015,0.002414542],"genre_scores_gemma":[0.9830157,0.0002524955,0.01154918,0.0002336784,0.0001398989,0.00008553547,0.004059861,0.00001374271,0.0006498584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006367283,"threshold_uncertainty_score":0.01467192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006516273914382201,"score_gpt":0.3165031027943551,"score_spread":0.3099868288799729,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}